A study of metaheuristic algorithms for high dimensional feature selection on microarray data

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

doi:10.1063/1.5012198

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Metaheuristic algorithms are generally a higher-level technique that seeks to generate a sufficiently helpful solution to any optimization problem [ 50 ] that is complicated and challenging to solve to an optimal level. It has become vital to locate an optimal solution based on insufficient or partial data in the world of inadequate resources such as time and computational capacity.…”

Section: Introductionmentioning

confidence: 99%

“…The following questions were formulated to answer the question above. A limited related study has been done on metaheuristic feature selection on the multiclass problem [50,261,266], which focuses on evolutionary computation approaches to feature selection and high dimensional feature selection on microarray data, respectively. Therefore, this study reviews various categories of metaheuristic algorithms in solving multiclass problems within two decades (i.e., 2000-2022), e.g., human-based, physics-based, evolutionary-based, and swarm intelligence-based approaches.…”

Section: Introductionmentioning

confidence: 99%

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Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

Hamid

Sallehuddin

Yunos

et al. 2023

Neural Comput & Applic

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Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.

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MiRNA subset selection for microarray data classification using grey wolf optimizer and evolutionary population dynamics

Almotairi

2023

Neural Comput & Applic

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A study of metaheuristic algorithms for high dimensional feature selection on microarray data

Cited by 8 publications

References 16 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

MiRNA subset selection for microarray data classification using grey wolf optimizer and evolutionary population dynamics

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